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Uncertainty, ignorance and ambiguity in crop modelling for African agricultural adaptation

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Abstract

Drawing on social constructivist approaches to interpreting the generation of knowledge, particularly Stirling’s (Local Environ 4(2):111–135, 1999) schema of incomplete knowledge, this paper looks critically at climate-crop modelling, a research discipline of growing importance within African agricultural adaptation policy. A combination of interviews with climate and crop modellers, a meta-analysis survey of crop modelling conducted as part of the CGIAR’s Climate Change Agriculture and Food Security (CCAFS) programme in 2010, and peer-reviewed crop and climate modelling literature are analysed. Using case studies from across the crop model production chain as illustrations it is argued that, whilst increases in investment and growth of the modelling endeavour are undoubtedly improving observational data and reducing ignorance, the future of agriculture remains uncertain and ambiguous. The expansion of methodological options, assumptions about system dynamics, and divergence in model outcomes is increasing the space and need for more deliberative approaches to modelling and policy making. Participatory and deliberative approaches to science-policy are advanced in response. The discussion highlights the problem that, uncertainty and ambiguity become hidden within the growing complexity of conventional climate and crop modelling science, as such, achieving the transparency and accessibility required to democratise climate impact assessments represents a significant challenge. Suggestions are made about how these challenges might be responded to within the climate-crop modelling community.

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Notes

  1. Based on the National Research Council’s model of democratic policy making (Stern and Fineberg 1996)

  2. See Ferretti and Pavone (2009) for two recent case studies of this model of risk governance

  3. Parry et al. (2004), Tatsumi et al. (2011), Fischer et al. (2009), Thornton et al. (2011, 2009), and Nelson et al. (2009)

  4. Web of Knowledge and Science Direct;

    Search in ‘title and abstract’ (SD) or ‘topic’ (WoK): ((climate OR precipitation OR temperature OR crop OR yield OR productivity OR agricult* OR maize) AND (process OR trend OR projection OR forecast)) AND model AND (Kenya OR “East* Africa”) AND (uncertain* OR assum* OR unknown)

    Screening criteria: Specific focus on Kenya of East Africa; Refers to projections of climate or maize crop models; Published since 1992; Identifies sources of uncertainty in projections

  5. A description of the methodology and results of this survey can be found at http://ccafs.cgiar.org/news/decision-support/crop-modelling-climate-change-and-food-security

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Authors and Affiliations

  1. Institute of Development Studies, Library Road, Brighton, BN1 9RE, UK

    Stephen Whitfield

  2. World Agroforestry Centre, UN Avenue, Nairobi, Kenya

    Stephen Whitfield

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Whitfield, S. Uncertainty, ignorance and ambiguity in crop modelling for African agricultural adaptation. Climatic Change 120, 325–340 (2013). https://doi.org/10.1007/s10584-013-0795-3

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